code: def normalize_indices(
      indices: Optional[List[int]], d: int
  ) -> Optional[List[int]]:
      r"""Normalize a list of indices to ensure that they are positive.
  
      Args:
          indices: A list of indices (may contain negative indices for indexing
              "from the back").
          d: The dimension of the tensor to index.
  
      Returns:
          A normalized list of indices such that each index is between `0` and
          `d-1`, or None if indices is None.
      """
      if indices is None:
          return indices
      normalized_indices = []
      for i in indices:
          if i < 0:
              i = i + d
          if i < 0 or i > d - 1:
              raise ValueError(
                  f"Index {i} out of bounds for tensor or length {d}."
              )
          normalized_indices.append(i)
      return normalized_indices
  
file_path: botorch/botorch/utils/transforms.py
imports: from __future__ import annotations
  import warnings
  from functools import wraps
  from typing import Any, Callable, List, Optional, TYPE_CHECKING
  import torch
  from botorch.utils.safe_math import logmeanexp
  from torch import Tensor
label_desc: Use image node_id 4 for calling a global function with example usage: normalize_indices(indices, d) and returns: normalized_indices, indices